Discovery, mapping, and scoring of machine learning models residing on an external application from within a data pipeline

ABSTRACT

A data pipeline configuration system allows industrial data pipelines to be configured using an intuitive visual interface. The pipeline configuration system allows graphical pipeline components representing data sources, data processing, analytic or machine learning models, and emitters to be selectively added to an industrial data pipeline application by selecting these components from a library. The pipeline configuration application is created by arranging and linking these selected pipeline components within a pipeline builder section of the configuration system&#39;s visual design interface. The design interface also allows analytic or machine learning models to be easily integrated into the pipeline application and mapped to incoming data items, such that the model is applied and scored against incoming data during pipeline operation. The configuration system also allows the user to configure destinations or data sinks for the pipeline data, including both the incoming industrial data and model scoring results.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationSer. No. 63/164,786, filed on Mar. 23, 2021, and entitled “INDUSTRIALDATA PIPELINE CONFIGURATION SYSTEM,” the entirety of which isincorporated herein by reference.

BACKGROUND

The subject matter disclosed herein relates generally to industrial dataprocessing and transformation, and, for example, to the configuration ofdata pipelines

BRIEF DESCRIPTION

The following presents a simplified summary in order to provide a basicunderstanding of some aspects described herein. This summary is not anextensive overview nor is it intended to identify key/critical elementsor to delineate the scope of the various aspects described herein. Itssole purpose is to present some concepts in a simplified form as aprelude to the more detailed description that is presented later.

In one or more embodiments, a system is provided, comprising a userinterface component configured to render an interface display and toreceive, via interaction with the interface display, pipelineconfiguration input that defines aspects of an industrial data pipeline,the pipeline configuration input comprising at least model selectioninput that selects an analytic model to be included in the industrialdata pipeline, and mapping input that maps selected items of input datafrom a data source to corresponding input fields of the analytic model;and a pipeline configuration component configured to generate pipelineapplication data based on the pipeline configuration input wherein thepipeline application data configured to execute on a hardware platformto implement the industrial data pipeline, and the industrial datapipeline is configured to apply the analytic model to the selected itemsof the input data in accordance with the pipeline configuration input.

Also, one or more embodiments provide a method, comprising rendering, bya system comprising a processor, an interface display on a clientdevice; receiving, by the system via interaction with the interfacedisplay, pipeline configuration input that defines aspects of anindustrial data pipeline, wherein the receiving comprises: receiving atleast model selection input that selects an analytic or machine learningmodel to be included in the industrial data pipeline, and receivingmapping input that maps selected items of input data from a data sourceto respective input fields of the analytic or machine learning model;and generating, by the system, a data pipeline application in accordancewith the pipeline configuration input, wherein the generating comprises,based on the model selection and the mapping input, configuring the datapipeline application to apply the analytic or machine learning model tothe selected items of the input data, and the data pipeline applicationis configured to execute on a hardware device to implement theindustrial data pipeline.

Also, according to one or more embodiments, a non-transitorycomputer-readable medium is provided having stored thereon instructionsthat, in response to execution, cause a system comprising a processor toperform operations, the operations comprising rendering an interfacedisplay on a client device; receiving, via interaction with theinterface display, pipeline configuration input that defines aspects ofan industrial data pipeline, wherein the receiving comprises: receivingat least model selection input that selects an analytic or machinelearning model to be included in the industrial data pipeline, andreceiving mapping input that maps selected items of input data from adata source to respective input fields of the analytic or machinelearning model; and generating a data pipeline application in accordancewith the pipeline configuration input, wherein the generating comprises,based on the model selection and the mapping input, configuring the datapipeline application to apply the analytic or machine learning model tothe selected items of the input data, and the data pipeline applicationis configured to execute on a hardware platform to operate theindustrial data pipeline.

To the accomplishment of the foregoing and related ends, certainillustrative aspects are described herein in connection with thefollowing description and the annexed drawings. These aspects areindicative of various ways which can be practiced, all of which areintended to be covered herein. Other advantages and novel features maybecome apparent from the following detailed description when consideredin conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example industrial control environmentwithin a plant operational technology (OT) network in conjunction withrepresentative components of an information technology (IT) network.

FIG. 2 is a block diagram of a pipeline configuration system.

FIG. 3 is a diagram illustrating a generalized data pipeline that can beimplemented using embodiments of the pipeline configuration system.

FIG. 4 is a diagram illustrating selection of pipeline components forinclusion in a pipeline application using a pipeline configurationsystem.

FIG. 5 is an example user interface display that can be generated by apipeline configuration system and used to select pipeline components forinclusion in a pipeline application.

FIG. 6 is a user interface display in which a data preparation componenthas been added to a pipeline design in a pipeline builder section.

FIG. 7 is a diagram illustrating selection of analytic models forinclusion in a pipeline design.

FIG. 8 is an example interface display that can be generated by apipeline configuration system and used to browse available models forinclusion in a pipeline design.

FIG. 9 is a diagram illustrating submission of mapping data thatselectively maps items of an incoming data stream to inputs of aselected data model.

FIG. 10 is an example field mapping interface display that can begenerated by a pipeline configuration system and used to perform modelfield mapping.

FIG. 11 is an example model configuration interface display that can begenerated by a pipeline configuration system and used to setconfigurable parameters supported by an analytic model.

FIG. 12 is a diagram illustrating configuration of an emitter componentwithin a development environment of a pipeline configuration system.

FIG. 13 is an example emitter configuration display that can be used toconfigure a data pipeline's emitter properties.

FIG. 14 is a diagram illustrating deployment of a pipeline applicationby a pipeline configuration system.

FIG. 15 is a diagram illustrating execution of a pipeline applicationusing local processing resources of a pipeline configuration system.

FIG. 16 is an example machine learning (ML) model scoring display thatcan be rendered by a pipeline configuration system and used to renderprediction and scoring results and other information relating toapplication of an analytic model to incoming pipeline data.

FIG. 17a is a flowchart of a first part of an example methodology fordeveloping an industrial data pipeline application.

FIG. 17b is a flowchart of a second part of the example methodology fordeveloping an industrial data pipeline application.

FIG. 17c is a flowchart of a third part of the example methodology fordeveloping an industrial data pipeline application.

FIG. 17d is a flowchart of a fourth part of the example methodology fordeveloping an industrial data pipeline application.

FIG. 18 is a flowchart of an example methodology for executing a datapipeline application.

FIG. 19 is an example computing environment.

FIG. 20 is an example networking environment.

DETAILED DESCRIPTION

The subject disclosure is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding thereof. It may be evident, however, that the subjectdisclosure can be practiced without these specific details. In otherinstances, well-known structures and devices are shown in block diagramform in order to facilitate a description thereof.

As used in this application, the terms “component,” “system,”“platform,” “layer,” “controller,” “terminal,” “station,” “node,”“interface” are intended to refer to a computer-related entity or anentity related to, or that is part of, an operational apparatus with oneor more specific functionalities, wherein such entities can be eitherhardware, a combination of hardware and software, software, or softwarein execution. For example, a component can be, but is not limited tobeing, a process running on a processor, a processor, a hard disk drive,multiple storage drives (of optical or magnetic storage medium)including affixed (e.g., screwed or bolted) or removable affixedsolid-state storage drives; an object; an executable; a thread ofexecution; a computer-executable program, and/or a computer. By way ofillustration, both an application running on a server and the server canbe a component. One or more components can reside within a processand/or thread of execution, and a component can be localized on onecomputer and/or distributed between two or more computers. Also,components as described herein can execute from various computerreadable storage media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry which is operated by asoftware or a firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can include a processor therein to executesoftware or firmware that provides at least in part the functionality ofthe electronic components. As further yet another example, interface(s)can include input/output (I/O) components as well as associatedprocessor, application, or Application Programming Interface (API)components. While the foregoing examples are directed to aspects of acomponent, the exemplified aspects or features also apply to a system,platform, interface, layer, controller, terminal, and the like.

As used herein, the terms “to infer” and “inference” refer generally tothe process of reasoning about or inferring states of the system,environment, and/or user from a set of observations as captured viaevents and/or data. Inference can be employed to identify a specificcontext or action, or can generate a probability distribution overstates, for example. The inference can be probabilistic—that is, thecomputation of a probability distribution over states of interest basedon a consideration of data and events. Inference can also refer totechniques employed for composing higher-level events from a set ofevents and/or data. Such inference results in the construction of newevents or actions from a set of observed events and/or stored eventdata, whether or not the events are correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B. In addition, the articles “a” and“an” as used in this application and the appended claims shouldgenerally be construed to mean “one or more” unless specified otherwiseor clear from the context to be directed to a singular form.

Furthermore, the term “set” as employed herein excludes the empty set;e.g., the set with no elements therein. Thus, a “set” in the subjectdisclosure includes one or more elements or entities. As anillustration, a set of controllers includes one or more controllers; aset of data resources includes one or more data resources; etc.Likewise, the term “group” as utilized herein refers to a collection ofone or more entities; e.g., a group of nodes refers to one or morenodes.

Various aspects or features will be presented in terms of systems thatmay include a number of devices, components, modules, and the like. Itis to be understood and appreciated that the various systems may includeadditional devices, components, modules, etc. and/or may not include allof the devices, components, modules etc. discussed in connection withthe figures. A combination of these approaches also can be used.

Industrial controllers, their associated I/O devices, motor drives, andother such industrial devices are central to the operation of modernautomation systems. Industrial controllers interact with field deviceson the plant floor to control automated processes relating to suchobjectives as product manufacture, material handling, batch processing,supervisory control, and other such applications. Industrial controllersstore and execute user-defined control programs to effectdecision-making in connection with the controlled process. Such programscan include, but are not limited to, ladder logic, sequential functioncharts, function block diagrams, structured text, or other suchplatforms.

FIG. 1 is a block diagram of an example industrial control environment100. In this example, a number of industrial controllers 118 aredeployed throughout an industrial plant environment to monitor andcontrol respective industrial systems or processes relating to productmanufacture, machining, motion control, batch processing, materialhandling, or other such industrial functions. Industrial controllers 118typically execute respective control programs to facilitate monitoringand control of industrial devices 120 making up the controlledindustrial assets or systems (e.g., industrial machines). One or moreindustrial controllers 118 may also comprise a soft controller executedon a personal computer or other hardware platform, or on a cloudplatform. Some hybrid devices may also combine controller functionalitywith other functions (e.g., visualization). The control programsexecuted by industrial controllers 118 can comprise any conceivable typeof code used to process input signals read from the industrial devices120 and to control output signals generated by the industrialcontrollers, including but not limited to ladder logic, sequentialfunction charts, function block diagrams, or structured text.

Industrial devices 120 may include both input devices that provide datarelating to the controlled industrial systems to the industrialcontrollers 118, and output devices that respond to control signalsgenerated by the industrial controllers 118 to control aspects of theindustrial systems. Example input devices can include telemetry devices(e.g., temperature sensors, flow meters, level sensors, pressuresensors, etc.), manual operator control devices (e.g., push buttons,selector switches, etc.), safety monitoring devices (e.g., safety mats,safety pull cords, light curtains, etc.), and other such devices. Outputdevices may include motor drives, pneumatic actuators, signalingdevices, robot control inputs, valves, and the like. Some industrialdevices, such as industrial device 120M, may operate autonomously on theplant network 116 without being controlled by an industrial controller118.

Industrial controllers 118 may communicatively interface with industrialdevices 120 over hardwired or networked connections. For example,industrial controllers 118 can be equipped with native hardwired inputsand outputs that communicate with the industrial devices 120 to effectcontrol of the devices. The native controller I/O can include digitalI/O that transmits and receives discrete voltage signals to and from thefield devices, or analog I/O that transmits and receives analog voltageor current signals to and from the devices. The controller I/O cancommunicate with a controller's processor over a backplane such that thedigital and analog signals can be read into and controlled by thecontrol programs. Industrial controllers 118 can also communicate withindustrial devices 120 over the plant network 116 using, for example, acommunication module or an integrated networking port. Exemplarynetworks can include the Internet, intranets, Ethernet, DeviceNet,ControlNet, Data Highway and Data Highway Plus (DH/DH+), Remote I/O,Fieldbus, Modbus, Profibus, wireless networks, serial protocols, and thelike. The industrial controllers 118 can also store persisted datavalues that can be referenced by the control program and used forcontrol decisions, including but not limited to measured or calculatedvalues representing operational states of a controlled machine orprocess (e.g., tank levels, positions, alarms, etc.) or captured timeseries data that is collected during operation of the automation system(e.g., status information for multiple points in time, diagnosticoccurrences, etc.). Similarly, some intelligent devices—including butnot limited to motor drives, instruments, or condition monitoringmodules—may store data values that are used for control and/or tovisualize states of operation. Such devices may also capture time-seriesdata or events on a log for later retrieval and viewing.

Industrial automation systems often include one or more human-machineinterfaces (HMIs) 114 that allow plant personnel to view telemetry andstatus data associated with the automation systems, and to control someaspects of system operation. HMIs 114 may communicate with one or moreof the industrial controllers 118 over a plant network 116, and exchangedata with the industrial controllers to facilitate visualization ofinformation relating to the controlled industrial processes on one ormore pre-developed operator interface screens. HMIs 114 can also beconfigured to allow operators to submit data to specified data tags ormemory addresses of the industrial controllers 118, thereby providing ameans for operators to issue commands to the controlled systems (e.g.,cycle start commands, device actuation commands, etc.), to modifysetpoint values, etc. HMIs 114 can generate one or more display screensthrough which the operator interacts with the industrial controllers118, and thereby with the controlled processes and/or systems. Exampledisplay screens can visualize present states of industrial systems ortheir associated devices using graphical representations of theprocesses that display metered or calculated values, employ color orposition animations based on state, render alarm notifications, oremploy other such techniques for presenting relevant data to theoperator. Data presented in this manner is read from industrialcontrollers 118 by HMIs 114 and presented on one or more of the displayscreens according to display formats chosen by the HMI developer. HMIsmay comprise fixed location or mobile devices with either user-installedor pre-installed operating systems, and either user-installed orpre-installed graphical application software.

Some industrial environments may also include other systems or devicesrelating to specific aspects of the controlled industrial systems. Thesemay include, for example, one or more data historians 110 that aggregateand store production information collected from the industrialcontrollers 118 and other industrial devices.

Industrial devices 120, industrial controllers 118, HMIs 114, associatedcontrolled industrial assets, and other plant-floor systems such as datahistorians 110, vision systems, and other such systems operate on theoperational technology (OT) level of the industrial environment. Higherlevel analytic and reporting systems may operate at the higherenterprise level of the industrial environment in the informationtechnology (IT) domain; e.g., on an office network 108 or on a cloudplatform 122. Such higher level systems can include, for example,enterprise resource planning (ERP) systems 104 that integrate andcollectively manage high-level business operations, such as finance,sales, order management, marketing, human resources, or other suchbusiness functions. Manufacturing Execution Systems (MES) 102 canmonitor and manage control operations on the control level givenhigher-level business considerations. Reporting systems 106 can collectoperational data from industrial devices on the plant floor and generatedaily or shift reports that summarize operational statistics of thecontrolled industrial assets.

Because of the large number of system variables that must be monitoredand controlled in near real-time, industrial automation systems oftengenerate vast amounts of near real-time data. In addition to productionstatistics, data relating to machine health, alarm statuses, operatorfeedback, electrical or mechanical load over time, and the like areoften monitored, and in some cases recorded, on a continuous basis. Thisdata is generated by the many industrial devices that make up a typicalautomation system, including the industrial controller and itsassociated I/O, telemetry devices for near real-time metering, motioncontrol devices (e.g., drives for controlling the motors that make up amotion system), visualization applications, lot traceability systems(e.g., barcode tracking), etc. Moreover, since many industrialfacilities operate on a 24-hour basis, their associated automationsystems can generate a vast amount of potentially useful data at highrates. The amount of generated automation data further increases asadditional plant facilities are added to an industrial enterprise.

To gain insights into the operation of plant-floor assets, automationsystems, and processes, this high-density industrial data can becollected and streamed to an analytics, visualization, or reportingsystem via a data pipeline, or a network of parallel data pipelines.However, configuring such data pipelines is a specialized task, oftenrequiring the services of a data engineer or data scientist having ahigh level of expertise.

Also, in some scenarios, pretrained analytic models, such as machinelearning models, may be available for generating insights or predictionsrelating to plant floor operations based on analysis of a specifiedsubset of data generated by plant floor devices. However, if thesemodels were developed by an external engineer with no knowledge of theend user's available data or the schema and naming conventions for thatdata, integrating these analytic models into a data-contextualizedenterprise-specific data pipeline can be challenging.

To address these and other issues, one or more embodiments describedherein provide a data pipeline configuration system that allows datapipelines to be configured using an intuitive visual interface. Thepipeline configuration system allows graphical pipeline componentsrepresenting data sources, data processing, analytic and machinelearning (ML) models, and emitters to be selectively added to a datapipeline application by selecting these components from a preconfiguredlibrary, also referred to as a palette. The pipeline application iscreated by arranging and linking these selected pipeline componentswithin a graphical development interface rendered by the system. Thedevelopment interface also allows analytic or machine learning modelscreated and trained a priori to be easily imported into the pipelineapplication and mapped to incoming data items via adapters (e.g.,channels) of various data sources, such that the model is scored againstincoming data during pipeline operation. The configuration system alsoallows the user to configure the pipeline to publish selected data itemsand model scoring results to specified destinations or data sinks.

FIG. 2 is a block diagram of a pipeline configuration system 202according to one or more embodiments of this disclosure. Aspects of thesystems, apparatuses, or processes explained in this disclosure canconstitute machine-executable components embodied within machine(s),e.g., embodied in one or more computer-readable mediums (or media)associated with one or more machines. Such components, when executed byone or more machines, e.g., computer(s), computing device(s), automationdevice(s), virtual machine(s), etc., can cause the machine(s) to performthe operations described.

Pipeline configuration system 202 can include a user interface component204, a pipeline configuration component 206, a model mapping component208, a pipeline deployment component 210, a data transformationcomponent 212, a data scoring component 214, a data publishing component216, a network interface component 218, one or more processors 220, andmemory 224. In various embodiments, one or more of the user interfacecomponent 204, pipeline configuration component 206, model mappingcomponent 208, pipeline deployment component 210, data transformationcomponent 212, data scoring component 214, data publishing component216, network interface component 218, the one or more processors 220,and memory 224 can be electrically and/or communicatively coupled to oneanother to perform one or more of the functions of the pipelineconfiguration system 202. In some embodiments, components 204, 206, 208,210, 212, 214, 216, and 218 can comprise software instructions stored onmemory 224 and executed by processor(s) 218. Pipeline configurationsystem 202 may also interact with other hardware and/or softwarecomponents not depicted in FIG. 2. For example, processor(s) 220 mayinteract with one or more external user interface devices, such as akeyboard, a mouse, a display monitor, a touchscreen, or other suchinterface devices.

User interface component 204 can be configured to generate userinterface displays that receive user input and render output to the userin any suitable format (e.g., visual, audio, tactile, etc.). In someembodiments, user interface component 204 can render these interfacedisplays on a client device (e.g., a laptop computer, tablet computer,smart phone, etc.) that is communicatively connected to the pipelineconfiguration system 202 (e.g., via a hardwired or wireless connection).Input data that can be received via user interface component 204 caninclude, but is not limited to, pipeline design input that selects andconfigures pipeline components and analytic models for inclusion in thepipeline, mapping input that maps selected data items to input fields ofa selected model, or other such input data. Output data rendered by userinterface component 204 can include, but is not limited to, pipelinecomponents and models that can be selectively integrated into a datapipeline configuration, parameters of analytic or machine learningmodels, model scoring results, or other such output data.

Pipeline configuration component 206 can be configured to generate anapplication based on pipeline configuration input received from a uservia user interface component 204. Model mapping component 208 can beconfigured to map selected input data variables from one or more datasources—either batch or streaming data—to inputs of an analytic modelincluded in the data pipeline. Pipeline deployment component 210 can beconfigured to deploy the pipeline application generated by pipelineconfiguration component 206 to one or more nodes of a pipeline runtimeenvironment for execution.

Data transformation component 212 can be configured to transformincoming data to a format that can be understood by pipeline processingcomponents, and that can be mapped to an analytic model (such as amachine learning model) included in the data pipeline. Model scoringcomponent 214 can be configured to apply a selected analytic model tothe transformed data generated by data transformation component 212 togenerate a model scoring output, also referred to as a model prediction.Data publishing component 216 can be configured to publish results ofthe model scoring performed by the model scoring component 214 to aspecified data sink (e.g., a data repository, an application, an assetmodel, etc.) outside the pipeline development platform. Networkinterface component 218 can be configured to interface the pipelineconfiguration system 202 to one or more networks, allowing the user tobrowse for external analytic models or applications that are to beincorporated into the pipeline. These models can include, for example,machine learning models created and trained in external applications,which can then be selectively incorporated into the data pipeline usingthe development interface.

The one or more processors 220 can perform one or more of the functionsdescribed herein with reference to the systems and/or methods disclosed.Memory 224 can be a computer-readable storage medium that storescomputer-executable instructions and/or information for performing thefunctions described herein with reference to the systems and/or methodsdisclosed. Memory 224 can also store predefined pipeline components 222which can be selected and integrated into the pipeline design.

FIG. 3 is a diagram illustrating a generalized data pipeline that can beimplemented using embodiments of the pipeline configuration system 202.A data pipeline 306 can be implemented on one or more pipeline nodes orother hardware platforms capable of relaying or streaming aggregateddata 302 collected from one or more data sources to a destination 308,which may be a data repository (e.g., cloud-based storage), anapplication that consumes the data (e.g., an analytic or reportingsystem), an industrial control system, an asset model or digital twinused in connection with monitoring and controlling an industrial assetor system, or another type of data destination. In the exampleillustrated in FIG. 3, data 302 is collected from one or more types ofdata sources (e.g., databases, data historians, cloud-basedrepositories, industrial controllers, motor drives, telemetry devices,etc.) via one or more channels of the pipeline. The nodes that executethe pipeline may be server devices, restful API-based external calls,microservices executing on respective computer hardware platforms, orother such processing elements.

The pipeline 306 can include components that perform processing on thedata 302. This can include processing to transform the incoming data toa format that can be understood by downstream pipeline components, andthat can be mapped to analytic models or other data processing units orcomponents included in the pipeline. These downstream pipelinecomponents can also include a user's custom code (e.g., code written inPython, Java, or Scala). The pipeline 306 can also be configured toperform model-based analytics on selected sets of the data 302.

The pipeline configuration system 202 supports a visual design interfacethat allows elements of this pipeline 306 to be easily configured anddeployed. This can include interacting with a graphical developmentinterface to select processing and analytic components for inclusion inthe pipeline 306, map incoming data to analytic models that have beenadded to the pipeline, and configure the data emitters that publish dataand analytic results to selected external applications or data sinks.The resulting pipeline 306 is also capable of parsing incoming data 302to automatically learn the data's schema, and transforming the data to aformat that can be understood by other downstream processing componentsof the pipeline 306 and that can be easily mapped to the succeedingprocessing components and analytic models (including but not limited tomachine learning models).

As noted above, pipeline configuration system 202 can provide visualdesign tools that guide the user through an intuitive workflow forcreating data pipeline applications, which can then be deployed andexecuted on hardware nodes running a highly scalable parallelizedruntime engine. This design workflow uses graphical icons representingprocessing and analytic components, which can be selected for inclusionin the data pipeline and configured within the system's graphicaldevelopment environment. FIG. 4 is a diagram illustrating selection ofpipeline components 410 for inclusion in a pipeline application 412. Ingeneral, the system's pipeline configuration component 206 generates apipeline application 412 based on design input submitted by the user,and updates this application 412 accordingly based on the receiveddesign input. The pipeline application 412 comprises components thatingress data that, when deployed and executed on one or more executioncluster nodes, performs the data transfer, processing, analytic, andemitter functions specified by the pipeline configuration.

Pipeline configuration system 202 can include a component library 406that stores various processing and analytic components 222 that can beselectively included in the pipeline application being configured.Library 406 can also include machine learning model creation components.Through interaction with the development environment generated by userinterface component 204 and served to a client device 402, a user cansubmit component selection data 404 that selects a subset of availablepipeline components 222 from the library 406 (or palette) for inclusionin the pipeline application 412. FIG. 5 is an example user interfacedisplay 502 that can be generated by user interface component 204 andused to select pipeline components 222 for inclusion in a pipelineapplication. Display 502 includes a pipeline builder section 508 (alsoreferred to as a canvas), a component selection section 512, a componentconfiguration section 510, and a user action palette 520. The useraction palette 520 allows the user to select and initiate a number ofhigh-level instructions, such as creating a new pipeline, saving thecurrent pipeline, and uploading the current pipeline. Pipelinecomponents 222 that can be selected for inclusion in the pipelineapplication are represented by components icons 506 in the componentselection section 512. Selection of a component icon 506 from thecomponent selection section 512 causes a graphical representation of thepipeline component 222 corresponding to the selected icon 506 to appearin the pipeline builder section 508. In some embodiments, selectedcomponent icons 506 can be dragged from the component selection section512 to the pipeline builder section 508 to facilitate adding thepipeline components 222 corresponding to the selected icons 506 to thepipeline application 412. components 222 can also be added using otherinteractions with the development interface, including but not limitedto double-clicking or otherwise selecting the icons 506. Once in thepipeline builder section 508, a component 222 can be moved and arrangedby the user.

An icon representing pipeline component 222 can have an associated input514 and output 516. In the pipeline builder section 508, the user canarrange selected pipeline components 222 and selectively link outputs516 of components 222 to inputs 514 of other succeeding or downstreamcomponents 222 using connector lines 518. In this way, the data pipelinecan be designed by arranging and linking selected pipeline components222 in an order that will be validated during design-time, yielding adata flow definition with discrete processing units. In some designscenarios, the order of the components 222 within the pipeline buildersection 508 can determine the order of data preparation, processing,transformation, analytics, or machine learning model execution that willbe carried out by the pipeline represented by the application 412, suchthat these functions will be executed in a cadence within the scope ofthe pipeline application 412 when deployed and executed on a runtimeexecution engine.

Pipeline components 222 can represent various types of entities,processing, analytics, or ML model applications that the user wishes toinclude in the pipeline. In a typical pipeline application, theleft-most components in the pipeline representation can be a data sourcecomponent 222 a representing a data source for the data 302 that will bebatched or streamed through the pipeline. The data source can be anindustrial device (e.g., an industrial controller, a variable frequencydrive, etc.), a data historian, a file system from which data isretrieved, an edge device (e.g., edge device 304) that collects inputdata and places the data on the pipeline, one or more industrial devicesoperating in a plant facility (e.g., industrial controllers, motordrives, sensors, telemetry devices, etc.), another application thatgenerates data to be placed on the pipeline (e.g., via nativeconnectivity), in-memory message queues or persistent stores that storedata accumulated from an industrial controller or edge device, adatabase, a data warehouse, a data lake within a same network or on acloud platform, rest application programming interface (API) calls, asoftware development kit (SDK) integrated into the system 202 or from ashared file system on the same network on which the execution runtimeengine is deployed, or other such data sources. The data from any ofthese data sources is ingested into the memory of pipeline configurationsystem 202 for further use by succeeding data pipeline components. Thecomponent library 406 can include a variety of data source components222 a representing different types of data sources, which can beselectively added to the pipeline design and configured to map to theuser's data source.

The right-most pipeline component 222 can be an emitter component 222 brepresenting a destination or data sink to which data ingested andprocessed by the pipeline is to be published. This destination can besubstantially any external application or system that consumes the data302 or processing results that are output by the pipeline. In someembodiments, pipeline configuration system 202 can support adapters orcustom-coded emitters designed to interface with specific types of datasinks, and which are associated with respective emitter components 222b. Example destinations for the pipeline output data can include, butare not limited to, visualization systems that visualize the pipelineoutput, reporting systems, other analytic systems, message brokersystems that send notifications to specified recipients based onanalytic results output by the pipeline, a control application on theplant floor that makes adjustments to an industrial process based on theanalytic results, or other such destinations. As with the data sources,the component library 406 can include emitter components 222 brepresenting a variety of different data destinations or data sinks,which can be added to the pipeline application and configured such thatselected data items processed by the pipeline are mapped to specifieddata points of the data destination entity.

Intermediate pipeline components 222 between the left-most andright-most components 222 can represent selected types of processing,analytics, or machine learning models to be applied to the data. Exampleprocessing components 222 can include, but are not limited to,components for cleaning the data (e.g., by detecting and removingoutlier data), transforming the data from an input format to a specifiedtarget format, renaming data items, or performing other such dataprocessing. These various types of data processing can be represented bypipeline components 222 stored in library 406, which can be selectivelyadded to the pipeline design and configured to perform their processingfunctions on selected sets of data 302.

As will be described in more detail below, pipeline configuration system202 can also allow analytic models, such as machine learning models, tobe added to the pipeline configuration as pipeline components 222 andmapped to selected sets of pipeline data.

Once a pipeline component 222 has been added to the pipeline buildersection 508, selection of one of the pipeline components 222 within thepipeline builder section 508 causes configuration information for theselected component 222 to be displayed in the component configurationsection 510. The configuration information displayed in the componentconfiguration section 510 depends on the type of component 222 selected.In the example depicted in FIG. 5, the data source component 222 a hasbeen selected. If this data source component 222 a has been linked toits corresponding data source, the component configuration section 510displays information about the data items available in that data source,including the name and data type of each data item and sample valuesread from the data items. Other types of pipeline components 222 willrender different sets of attributes and configurable parametersassociated with those components. For a given pipeline component 222,some of the component attributes displayed in the componentconfiguration section 510 may be configurable by the user, such that thevalues of these attributes can be changed via interaction with thecomponent configuration section 510 (e.g., by overwriting the displayedvalues of the attribute).

Different data sources can generate data 302 that accords to their ownparticular data schema, which determine the data items that areavailable from the data source, the information model naming conventionsor data tag names used to identify the data items, the data types of therespective data items, etc. The data schema of incoming data 302 may notmatch the format or schema requirement of the external application thatwill be consuming the batched or streamed data from the pipeline emittercomponent. To address this issue, the pipeline component library 406 caninclude data preparation components capable of parsing incoming data 302from a specified data source, identifying the data's schema, andconverting the data 302 to a format that can be understood and used bydownstream pipeline processing components and, eventually, by theexternal application or data sink that will consume the data from thedata pipeline.

FIG. 6 is a view of user interface display 502 in which a datapreparation component 222 c has been added to a pipeline design in thepipeline builder section 508. The input of the data preparationcomponent 222 c has been linked to the output of a data generatorcomponent representing a source of data 302 that is to be placed on thepipeline. In some embodiments, pipeline configuration system 202 canoffer different types of data preparation components 222 c correspondingto respective different types of applications for which the data isbeing prepared. Configuration information for the data preparationcomponent 222 c is displayed in the component configuration section 510.This information can include connection and file path information forthe incoming data 302 and/or the external application for which the datais being prepared, a name of the converted data set that will be outputby the data preparation component 222 c, or other configurationinformation.

Adding the data preparation component 222 c to the pipeline path insertsa data preparation processing function into the pipeline. Thisprocessing scans or parses the data 302 received from the data source,identifies the data's schema, and based on this knowledge of the data'sschema, converts the incoming data 302 to a format that can be used bydownstream pipeline processors. In an example scenario, the datapreparation processing can receive unstructured data 302, or data thatis structured according to a schema that is incompatible with adownstream application in the pipeline, and convert this incoming data302 to a comma-separated values (CSV) file understandable by thedownstream pipeline processor and, eventually, by the externalapplication or data sink that consumes the data egressed by the datapipeline. This comma-separated format can be understood and acted uponby downstream pipeline applications data processing units. Since thedata preparation processing performed by the data preparation component222 c auto-detects the schema of the incoming data, data engineers neednot design their data collection systems to pre-specify this incomingdata schema to the configuration system 202.

As noted above, the system 202 also allows analytic models, such asmachine learning models, to be added to the pipeline application 412 aspipeline components. FIG. 7 is a diagram illustrating selection ofanalytic models 702 for inclusion in the pipeline design. In someembodiments, the system 202 can allow a user to select from amongpretrained analytic or machine learning models stored on a model library704. In addition or alternatively, the system 202 can allow the user tobrowse for and select models 702 that are stored externally to theconfiguration system 202. These external models can be, for example,models executed by external applications 708 (e.g., third-partyapplications that offer analytic models that can be applied to end userdata) or models that were otherwise developed in a separate developmentsystem. In the example depicted in FIG. 7, the pipeline configurationsystem 202 is interfaced to a network 710 (e.g., a plant and/or officenetwork) via network interface component 218, and through thisconnection the system 202 allows the user to browse models stored on anexternal application 708 on the same network 710. In this way, users caneither select from a library 704 of locally stored analytic models 702offered by the configuration system 202, or import analytic models 702that were developed by external systems. Analytic models 702 from any ofthese sources can be selectively added to the pipeline application 412as a pipeline component 222.

FIG. 8 is an example interface display 802 that can be generated by userinterface component 204 and used to browse available models 702 forinclusion in a pipeline design. This example interface display 802includes a Connection Name field 806 that allows the user to specify aconnection to an external application having available analytic models.Connections to external applications having available analytic models702 can be defined using a connection tool supported by the pipelineconfiguration system 202. Once defined, these connections are selectablein the Connection Name field 806 via a drop-down window. Upon selectionof a connection to an external application, a model list area 804renders a list of analytic models that are available on the selectedexternal application. This list can render information about each model,including but not limited to a name and description of the model, tagsrepresenting the model's inputs and outputs, a current status of themodel (e.g., available, unavailable, etc.), a version number of themodel, or other such information. The user can then select one or moreof the models in the model list area 804 that are to be included in thepipeline, and in response to this selection the pipeline configurationcomponent 206 updates the pipeline application 412 to add the selectedmodels.

Substantially any type of analytic or machine learning models 702 can beincorporated into the pipeline application 412, including but notlimited to predictive models, binary classification models, statisticalmodels, regression analysis models, clustering models, decision trees,or other such analytic models. Models 702 may also comprise asset modelsor digital twins that digitally represent industrial assets in operationin the plant facility. In some embodiments, the pipeline configurationsystem 202 can also include model building tools that allow the user tocreate their own custom analytic models for inclusion in the pipelineapplication. In general, analytic or machine learning models 702 can betrained algorithms designed to analyze specified data inputs to performsuch functions as generating predictions regarding operation of anindustrial machine or process (e.g., predicting product output, atime-to-failure for a device or machine, energy consumption, machineemissions, etc.), identifying a modification to an industrial process orcontrol parameter that may optimize a performance metric of a controlledindustrial asset, calculate statistics regarding operation of anindustrial machine or process, or other such analytic functions.

A given analytic model 702 is designed to process a specific set of datainputs in order to generate its output. As part of the data pipelinedesign, configuration system 202 allows the user to map these definedmodel inputs, or fields, to corresponding data items of their incomingdata streams. FIG. 9 is a diagram illustrating submission of mappingdata 902 that selectively maps items of an incoming data stream toinputs of a selected data model. After a data source has been added tothe pipeline application 412 by adding a data source component 222 a,data preparation processing has been configured by adding andconfiguring a data preparation component 222 c, and an analytic model702 has been added to the pipeline design as described above inconnection with FIGS. 7 and 8, user interface component 204 can render afield mapping display that allows the user to submit mapping data 902.This mapping data 902 selects items of data from the specified datasource to corresponding input fields of the imported analytic model 702.To facilitate this mapping, the system's model mapping component 208 canparse the selected model 702 to identify the input fields defined forthe model 702, which represent the model's digital and/or analog inputs.As part of this model analysis, the model mapping component 208 canidentify the name and data type of each of the input fields defined bythe model 702. The user interface component 204 can present these inputfields to the user and allow the user to submit, as mapping data 902,selections of which data items generated by the pipeline's data sourcesare to be mapped to corresponding input fields of the analytic model702. Based on this mapping data 902, pipeline configuration component206 updates the pipeline application 412 to include the user's definedmodel input mapping 904.

FIG. 10 is an example field mapping interface display 1002 that can begenerated by user interface component 204 and used to perform the modelfield mapping described above in connection with FIG. 9. As noted above,the model mapping component 208 parses the analytic model 702 beingadded to the pipeline application 412 (e.g., the model 702 selectedusing interface display 802) to identify the input fields defined forthe model 702. The names of these input fields are listed in a ModelField column of display 1002, and the data types (e.g., Boolean, doublefloating point, integer, etc.) of each field are listed in an adjacentType column. Next to each listed input field, under an Input MessageField column, is a selection field 1004 (e.g., a drop-down selectionbox) that allows the user to select a data item from the pipeline's datasource that is to be mapped to that model input field.

The data item selections listed in the selection fields 1004 can bebased on the prepared data generated by the data preparation component222 c as a result of performing data preparation processing on the data302 from the pipeline's data source. For example, if the datapreparation component 222 c is configured to convert incoming data 302(either structured or unstructured data) to a CSV file that lists theavailable items of data 302 as comma-separated data tag names, the userinterface component 204 will populate the selection fields 1004 with thedata tag names read from this CSV file. Selection of a data item in aselection field 1004 of the Input Message Field column causes theselected data item to be mapped to the corresponding model field entryin the Model Field column. To ensure that the user has mapped a data taghaving the same data type as that of the model field, an Input FieldType column can display the data type of the data item selected in theInput Message Field column. In some embodiments, if the data type of theselected data item does not match that of the model field to which it ismapped, the user interface component 204 can render an alarm message1006 indicating that the data types of the model's input field and itscorresponding data item must match.

Since the analytic model 702 may have been developed by a third partywith no knowledge of the user's data schema or naming convention, thisapproach can allow the user to perform a selective one-to-one mappingbetween each input field of the model and a data item selected by theuser as corresponding to that field. Items of data 302 that can bemapped to the model's input fields can include, for example, measuredprocess values generated by telemetry devices (e.g., temperatures,pressures, flows, motor speeds, etc.), alarm indicators (e.g., lowgrease alarms, high temperature alarms, etc.), device or machinestatuses (e.g., running, idle, faulted, etc.), or other such data items.Depending on the number of input fields defined for the model 702, thetotal number of data items mapped to the model 702 may be less than thetotal number of data items available from the pipeline's data source.

Some analytic or machine learning (ML) models 702 may have additionalparameters that can be set by the user to improve their model algorithmperformance. These model-specific parameters can be identified by themodel mapping component 208 based on the parsing of the model 702 by themodel mapping component 208 and rendered by the user interface component204 for review and modification by the user. FIG. 11 is an example modelconfiguration interface display 1102 that can be generated by the userinterface component 204 and used to set any configurable parameterssupported by the model 702. In this example, interface display 1102renders a set of model configuration fields 1104 that allow the user toenter values of configurable parameters for a selected analytic model702 (named “vibration model”). These fields 1104 can include, but arenot limited to, a Goal field specifying a data item (e.g., “s1_fb1”) tobe predicted or optimized by the model 702, an Identifier field, aCausal Technique field specifying a type of analysis or an analyticapproach to be used by the model 702, or other such model parameters.These parameters are identified by the model mapping component 208 basedon the initial parsing of the model 702. The user can interact withthese parameter fields to change values of these model parameters asdesired. The values of these parameters determine how the model 702performs its analysis of the data items mapped to the model's inputfields.

Although examples discussed above have considered importing andconfiguring an analytic or machine learning model 702 into the pipelineapplication 412 as a pipeline component 222, the pipeline configurationsystem 202 can also allow the user to import other types of externalapplications into the pipeline application 412 using a similar workflow.These external applications can be substantially any type of applicationcapable of performing processing, transformation, or analysis ofincoming data generated by the data source specified by the pipelinedesign.

As noted above with reference to FIG. 5, the right-most pipelinecomponent 222 in the pipeline builder section 508 can be an emittercomponent 222 b representing a destination or data sink for data oranalytic results generated by the pipeline. This emitter component 222 bhas associated parameters that can be set by the user to configure whichdata items or analytic results are to be published to the data sinkrepresented by the emitter component 222 b, and where this data is to besent. FIG. 12 is a diagram illustrating configuration of this emittercomponent 222 b. In response to selection of the emitter component 222b, the user interface component 204 can render an emitter configurationdisplay that allows the user to submit emitter configuration data 1202that sets the emitter configuration parameters for the pipeline beingdesigned. Based on this emitter configuration data 1202, the pipelineconfiguration component 206 updates the pipeline application 412 to addan emitter configuration 1204 that will instruct the pipeline nodes howto map the specified data items or analytic results to the data sinkrepresented by the emitter component 222 b.

FIG. 13 is an example emitter configuration display 1302 that can beused to configure the pipeline's emitter properties. As noted above,this configuration display 1302 can be invoked by selecting the emittercomponent 222 b within the pipeline builder section 508. The pipelinecomponent library 406 can include different types of emitter components222 b representing different types of data sinks to which the pipelinedata can be published, including but not limited to a data storagerepository (e.g., cloud-based storage, a database, or another type ofdata storage), a data queue of another data processing or analyticsystem, a messaging or notification system, a visualization system, anindustrial control application that makes adjustments to machine orprocess control based on analytic results generated by the pipeline, orother such data sinks. The specific emitter parameters rendered onconfiguration display 1302 may depend on the type of data sink beingconfigured.

In the example depicted in FIG. 13, a first set of configurationparameters 1306 can allow the user to specify a connection path to thedata sink entity to which selected items of pipeline output data are tobe published. In some scenarios, the data sink entity may be representedas an external object (e.g., a database, an analytic application, acontrol application, a reporting application, an industrial asset model,etc.) having properties, attributes, or data fields to which data can bewritten. Parameters 1306 can include connection path information to thedata sink and any additional information necessary to identify theentity to which the pipeline data will be published. In someembodiments, if the data sink resides on the same network as thepipeline configuration system 202, or is otherwise accessible to thesystem 202, the configuration display 1302 can allow the user to browseto and select the data sink entity to set the entity as the target forpipeline data.

Configuration parameters 1306 for the emitter can also include an ActionType field that allows the user to define an action to be taken by thedata sink entity based on the pipeline output data, or a specifiedcondition of the output data. For example, if the selected data sinkentity is a notification system, the emitter configuration display 1302can allow the user define a notification action to be taken by thenotification system if one or more items of the pipeline data (e.g., oneor more outputs of the model 702) satisfied a specified condition. Thecondition that is to trigger the notification action can also bespecified by the user via display 1302. In another example, if the datasink entity is a control application or digital asset model thatmonitors and controls one or more industrial assets, the user may definea control action that is to be taken by the control application based onvalues of one or more of the pipeline data items. If the model 702included in the pipeline is a predictive model, this workflow can allowthe user to define a control action to be taken based on a predictiveoutput of the model 702. In still another example, if the selected datasink entity is an analytic application, the emitter configurationdisplay 1302 can allow the user to define an analytic action to be takenon one or more items of output data from the pipeline (e.g., a type ofanalysis to be applied to the data). Based on the action defined by theuser via emitter configuration display 1302, the pipeline will deliverits output data to the specified data sink together with informationspecifying an action to be performed by the data sink, either on thedata or based on the data.

Based on the selected data sink, a mapping section 1304 of theconfiguration display 1302 can render available properties of theselected data sink to which pipeline data can be written. In the exampledepicted in FIG. 13, these data sink properties are listed in a webform, with the names of the available properties listed in a Propertycolumn and a data type of the respective properties listed in a BaseType column. These editable properties can be discovered by the pipelineconfiguration component 206 based on parsing the configurationparameters of the selected data sink. For example, if the selected datasink is an analytic or reporting application that receives and processesdata inputs, the pipeline configuration component 206 has a prioriknowledge and can list the corresponding data inputs in the mappingsection 1304. The selected data item may also be an industrial assetmodel, such as a digital twin or another digital representation of anindustrial asset (e.g., an industrial machine, device, automationsystem, or plant) that is used by an industrial automation system inconnection with monitoring and controlling its corresponding asset. Suchindustrial asset models may include model properties whose valuesdetermine certain control actions or strategies deployed by theautomation system for the asset. If such an asset model is selected asthe pipeline's data sink, the pipeline configuration component 206 canidentify the configurable properties defined for the asset model andlist these properties in the mapping section 1304.

Each data sink property listed in the mapping section 1304 also has anassociated input field 1308 listed in an Input Field column Input fields1308 can be drop-down windows that are populated with available dataitems that can be published by the data pipeline to the selected datasink. The available data items listed in the input fields 1308 can bebased on the other pipeline configuration information submitted by theuser in the previous configuration steps. For example, publishablepipeline data can include any of the data items (e.g., data 302)generated by the pipeline's data source and propagated through thepipeline, as well as analytic results or predictions available from anyof the analytic or machine learning models 702 that have been added tothe pipeline. Similar to the model mapping workflow described above inconnection with FIG. 10, selection of an available pipeline data outputin an input field 1308 corresponding to one of the data sink propertiesconfigures the pipeline to publish the selected data to that data sinkproperty.

Since the configuration system presents the user with a set of availabledata sink inputs that are specific to the selected type of data sink,based on the configuration system's a priori knowledge, and allows theuser to selectively map pipeline data items or model outputs to thesedata sink inputs, the resulting pipeline can deliver its data to theselected data sink in a format that is understood by the data sinkwithout the need to configure the data sink itself to interface with theincoming pipeline data

Since the pipeline emitter configuration allows the user to easily mapand egress analytic or machine learning model outputs to specifieddestinations, including control applications or asset models used insuch control applications, the configuration workflow supported by thepipeline configuration system 202 can allow the user to easily configurea closed-loop control architecture in which predictions generated by apredictive machine learning model are used as a basis for automaticallyupdating a control parameter of an industrial control system. In anexample scenario, a machine learning model 702 that has been added tothe data pipeline may be configured to infer values of one or morecontrol variables—e.g., a gas flow velocity, a valve pressure, a nozzlevelocity, etc.—that will optimize a performance metric of an industrialasset (e.g., maintain a specified furnace temperature, minimize anamount of waste produced by a production line, maximize productthroughput, minimize machine downtime, minimize energy consumption,minimize emissions, etc.) based on analysis of various monitoredvariables fed to the pipeline as incoming data 302 (e.g., ambienttemperature, current gas flow velocity, etc.). Using the design workflowdiscussed herein, the user can design the pipeline to map thesepredicted control variable values from the model 702 to the controlsystem that monitors and controls the relevant industrial asset; e.g.,by mapping the predicted values generated by the model 702 to theircorresponding data tags in an industrial controller, or to correspondingfields of a digital asset model, thereby altering control of the assetin accordance with the model to optimize the performance metric.

After the user has completed the design of pipeline application 412using the general workflow discussed above, validation tools supportedby the pipeline configuration system 202 can be used to validate theproposed pipeline design. These validation tools can be applied locallyon the pipeline configuration system 202 before the pipeline application412 is deployed and executed on the actual pipeline architecture. Invarious embodiments, this validation process can analyze the pipelineapplication 412 to verify that all model mapping and data sink mappingdefinitions are valid (e.g., that the mappings include no data typemismatches), verify that all necessary input fields of every model 702that has been integrated into the pipeline have been mapped, or performother such validations.

Once the pipeline application 412 has been validated, the application412 can be deployed to one or more pipeline nodes for execution. FIG. 14is a diagram illustrating deployment of the pipeline application 412.Pipeline deployment component 210 can compile or otherwise translate acompleted pipeline application 412 into one or more executable pipelineconfiguration files 1406 that can be stored and executed on one node ora cluster of nodes 1404 that make up the data pipeline architecture.These one or more nodes 1404 can be execution cluster nodes, serverdevices, microservices executing on respective computer hardwareplatforms, or other such processing elements. In general, the pipelineapplication 412 can be executed on a scalable parallelized runtimeengine that runs on a cluster of hardware nodes (e.g., nodes 1404), ormay run on a single such node. Pipeline deployment component 210 candeploy the compiled pipeline application 412 to these node devices 1404over a shared public or private network 1402. Alternatively, if thepipeline configuration system 202 is implemented on a hardware platformthat will act as a pipeline node, the pipeline application 412 need notbe deployed to an external node 1404, but rather can be complied andexecuted on the pipeline configuration system's own hardware platform.

Execution of the pipeline application 412 on a computing platform—e.g.,on nodes 1404 or using the pipeline configuration system's localprocessing resources—causes the computing platform to collect, process,analyze, and publish data in accordance with the pipeline design definedby the application 412. FIG. 15 is a diagram illustrating execution ofthe pipeline application 412 using local processing resources of thepipeline configuration system 202. The pipeline application 412 canimplement a data transformation component 212, a model scoring component214, and a data publishing component 216 that carry out pipelinefunctionality defined by the application 412. During operation, data 302is received from the one or more data sources defined by the application412 and transformed by the data transformation component 212 to yieldtransformed data 1502. In this regard, the data transformation component212 can parse the incoming data 302—which may be either structured orunstructured data—to determine the schema of the data 302, and transformthe data in accordance with the data preparation component 222 c thathad been added to the pipeline design (see FIG. 6). In someconfigurations, the data transformation component 212 can identify thedata items contained in the incoming data 302—including the names of thedata items, their data types, and their corresponding values—andgenerate, as the transformed data 1502, a CSV file that stores this datain a format that can be understood and processed by downstream pipelineprocessing elements.

If the pipeline application 412 includes an analytic or machine learningmodel 702 that had been added during the pipeline configuration process(e.g., using the workflow discussed above in connection with FIGS.7-11), a model scoring component 214 can pass a selected subset of thetransformed data 1502 a to the model 702 for processing. The items oftransformed data 1502 a passed to the model 702, and the model inputfields to which each data item is provided, are specified by the modelinput mapping 904 that had been entered by the user as discussed abovein connection with FIGS. 9 and 10. The model scoring component 214executes the model 702 on the mapped subset of the transformed data 1502a to yield a model output, or scoring results 1504. The type or formatof the scoring results 1504 generated by the model 702 depends on thetype of analysis for which the model 702 is designed, and may be aprediction regarding a future operation or status of an industrialsystem (e.g., an expected time-to-failure of an industrial machine ordevice, an predicted energy consumption by a machine, etc.), a value ofone or more control parameters expected to optimize a selectedperformance metric, a classification, or other such analytic results.

The pipeline configuration component 206 can be configured to rendermodel scoring results 1504 for a user's review while the pipeline isexecuting. FIG. 16 is an example model scoring display 1602 that can berendered by the user interface component 204 and used to visualizescoring results 1504 and other information relating to application ofthe analytic model 702 to incoming data 302. Display 1602 renders valuesof the incoming data 302 (read from the transformed data 1502 a, whichmay be a CSV file or another suitable format understandable by the modelscoring component 214) as well as scoring results generated by the modelscoring component 214 based on application of the model 702 to theincoming data. The example depicted in FIG. 16 renders scoring resultsfor a predictive model, and therefore includes a column displayingpredictive scores for two variables of interest (“s1_fb1” and“s1_fb1_mo”). Display 1602 can also render other information regardingthe scoring, including weights applied to the model's various datafields. The model fields and results rendered by display 1602 depend onthe chosen analytic model 702 and its configuration, as well as theaspect of the industrial process that is to be predicted by the model.Model scoring display 1602 serves as a preview panel for the finaloutput data before the data leaves the platform via the emitter.

In some scenarios, one or more of the models 702 added to the pipelineapplication 412 may be imported and executed locally on the pipelineconfiguration system 202 or remote nodes on which the pipelineapplication 412 executes. In such embodiments, addition of the model 702to the pipeline application 412, as described above in connection withFIGS. 7 and 8, can cause the selected model 702 to be imported into theconfiguration system 202 and integrated directly into the pipelineapplication 412. Alternatively, if a model 702 from an externalapplication 708 is imported to the pipeline application 412 (see FIG.7), incoming data from the data source can be mapped to the externalmodel 702 imported as described above, but the model 702 can execute onthe external application 708 without being imported directly into thepipeline application 412 or deployed to the nodes 1404. During executionof the data pipeline in such scenarios, the external application 708 canbe made a part of the data pipeline, such that the mapped transformeddata 1502 a is sent to the external application 708 for processing bythe application's analytic model 702, and the resulting scoring results1504 are returned by the external application 708 to the pipelineconfiguration system 202 (or node 1404) for traversal through the restof the data pipeline and publishing to the data sink.

Although the present example depicts the model scoring display 1602 canas being viewed during operation of the pipeline after the pipelineapplication 412 has been deployed, display 1602 can also be invokedduring validation of the pipeline application 412 before the pipeline isdeployed in order to verify the scoring results 1504 generated by themodel 702, as well as to confirm that incoming data items have beencorrectly mapped to the model's input fields. For example, duringvalidation of the pipeline application 412, the user may link thepipeline application 412 to a repository of stored historical data, orto the live data source, and execute the model on data obtained fromthese sources. The user can then invoke display 1602 to view the scoringresults 1504 generated by applying the model to this test data set. Inthis way, the user can verify that the model is generated expectedresults prior to deploying the application 412.

Returning to FIG. 15, the scoring results 1504 and transformed data 1502b are conveyed to the data publishing component 216, which is configuredto publish the scoring results 1504 and the transformed data to one ormore data sinks in accordance with the emitter configuration 1204submitted by the user (as discussed above in connection with FIGS. 12and 13). Data publishing component 216 publishes the scoring results1504 and items of transformed data 1502 b—together with any actioninstructions pre-specified by the user via emitter configuration display1306—as published data 1506, which is mapped to the data sink inaccordance with the emitter configuration. As discussed above, the datapublishing component 216 can publish the data 1506 to substantially anytype of data sink. For example, the data publishing component 216 maymap selected scoring results 1504 and items of transformed data 1502 bto corresponding attributes of a digital asset model used in connectionwith monitoring and controlling a corresponding industrial asset,thereby altering control of the asset based on predictions or controloptimization strategies generated by the model 702. In another examplescenario, the data publishing component 216 may map the published data1506 to control parameters of an industrial control program (e.g., aladder logic program executing on a programmable logic controller) tosimilarly effect a control modification based on the scoring results1504. Data publishing component 216 may also send the published data1506 to other types of data sinks, including but not limited to externalapplications (e.g., analytic or reporting applications), datahistorians, cloud-based archival storage, notification systems,visualization systems such as HMI applications, or other such datasinks.

Embodiments of the pipeline configuration system 202 described hereincan simplify the process of designing and deploying a data pipeline byproviding an intuitive visual workflow for adding and configuringchannels, connectors, data processors, analytic models, and emitters. Byguiding the user through the steps of creating a pipeline—includinglinking to data sources, adding data processors and analytic models, anddefining actions to be taken based on analytic results—the configurationsystem 202 can assist users with relatively little training in pipelinedevelopment in designing and deploying data pipelines.

FIGS. 17a -18 illustrate example methodologies in accordance with one ormore embodiments of the subject application. While, for purposes ofsimplicity of explanation, the methodologies shown herein is shown anddescribed as a series of acts, it is to be understood and appreciatedthat the subject innovation is not limited by the order of acts, as someacts may, in accordance therewith, occur in a different order and/orconcurrently with other acts from that shown and described herein. Forexample, those skilled in the art will understand and appreciate that amethodology could alternatively be represented as a series ofinterrelated states or events, such as in a state diagram. Moreover, notall illustrated acts may be required to implement a methodology inaccordance with the innovation. Furthermore, interaction diagram(s) mayrepresent methodologies, or methods, in accordance with the subjectdisclosure when disparate entities enact disparate portions of themethodologies. Further yet, two or more of the disclosed example methodscan be implemented in combination with each other, to accomplish one ormore features or advantages described herein.

FIG. 17a illustrates a first part of an example methodology 1700 a fordeveloping an industrial data pipeline application. Initially, at 1702,an interface display is rendered that permits selective addition of datapipeline components to a data pipeline application. This display can berendered by a pipeline configuration system, which is configured toguide a user through a visual pipeline development workflow and tocreate a pipeline application that can be compiled, deployed, andexecuted to facilitate implementation of a data pipeline. At 1704, adetermination is made as to whether a data source component is receivedvia interaction with the interface display rendered at step 1702. Thisdata source component can be selected from a library of pipelinecomponents accessible via the interface display, and can represent aspecific type of data source (e.g., a file system, an industrial device,an application, etc.). If a data source component is selected (YES atstep 1704), the methodology proceeds to 1706, where the selected datasource component is added to the data pipeline application in progress.Alternatively, if no data source component is selected (NO at step1704), the methodology proceeds to step 1708.

At 1708, a determination is made as to whether selection of a dataprocessing component is received via interaction with the interfacedisplay. The data processing component can also be selected from thelibrary of pipeline components, and can represent a selected type ofprocessing or data manipulation to be performed on data from the datasource added at step 1706, including but not limited to a specified typeof data formatting, a data conversion, a calculation, addition ofmetadata, or other such data processing. The present example assumesthat the selected data processing component is configured to parseincoming data from the data source to determine the data's schema, andto convert the incoming data to a format understandable by downstreampipeline components. If selection of a data processing component isreceived (YES at step 1708), the methodology proceeds to step 1710,where the selected data processing component is added to the datapipeline application. At 1712, a schema of data generated by the datasource represented by the data source component added at step 1706 isidentified. At 1714, the pipeline application is further configured, inaccordance with the data processing component, to convert the datagenerated by the data source to a format compatible with downstreampipeline components (e.g., a CSV file), where this conversion is basedon the identified schema. If no selection of a data processing componentis received (NO at step 1708) the methodology skips steps 1710-1714.

The methodology then proceeds to the second part 1700 b illustrated inFIG. 17b . At 1716, a determination is made as to whether an indicationof a connection path to an external location that stores one or moreanalytic models is received via interaction with the interface display(e.g., if a user has entered a connection path or has browsed to theexternal location on which the models are stored). If such a connectionpath is specified (YES at step 1716), the methodology proceeds to step1718, where a selectable list of the one or more analytic models arerendered on the interface display. This list is populated based on theanalytic models discovered at the external location identified by thecommunication path. At 1720, a determination is made as to whetherselection of an analytic model, of the listed analytic models, isreceived via interaction with the interface display. If such a modelselection is received (YES at step 1720), the methodology proceeds tostep 1722, where the analytic model selected at step 1720 is added tothe pipeline application.

Initially, the input fields of this selected analytic model may not bemapped to specific incoming data items, as in the case of an analyticmodel whose input fields were defined generically without knowledge ofthe naming conventions of the corresponding data items of a specificdata source or industrial application. Accordingly, at 1724, the datafields of the input fields of the selected analytic model areidentified. At 1726, a list of the input fields identified at step 1724are rendered on the interface display, together with associatedselection windows that facilitate selection of data items, from dataitems available from the data source, that are to be mapped to therespective input fields of the model. The data items made selectable viathe selection windows can be based on the data items discovered in thedata source represented by the data source component added at step 1706.In some scenarios, these data items may be discovered from a convertedor transformed version of the data source's data items generated by thedata processing component added at step 1710. The methodology thenproceeds to the third part 1700 c illustrated in FIG. 17 c.

At 1728, a determination is made as to whether selection of a data itemto be mapped to an input field of the model is received via interactionwith one of the selection windows rendered at step 1726. If such aselection is received (YES at step 1728), the methodology proceeds tostep 1730, where the data item selected at step 1728 is mapped to themodel input field corresponding to the selection window in which thedata item was selected. At 1732, a determination is made as to whethermodel mapping is complete. Model mapping may considered complete whenall input fields defined for the model have been mapped to a data itemusing steps 1728 and 1730. If model mapping is not complete (NO at step1732), the methodology returns to step 1728, and steps 1728 and 1730 arerepeated for another input field. Alternatively, if model mapping iscomplete (YES at step 1732), or if no model had been selected at step1720 (NO at step 1720), the methodology proceeds to step 1734.

At 1734, a determination is made as to whether selection of a dataemitter component is received via interaction with the displayinterface. The data emitter component can be selected from the componentlibrary made available by the pipeline configuration system, and canrepresent a specified type of data sink or destination to which outputdata from the pipeline is to be published (e.g., a data repository, ananalytic or reporting application, a messaging or notification system,an industrial control system, a digital asset model or digital twin ofan industrial system, etc.). If such a data emitter component isselected (YES at step 1734), the methodology proceeds to step 1736,where the data emitter component selected at step 1734 is added to thepipeline application. At 1738, emitter configuration input is receivedvia interaction with the interface display. This emitter configurationinput maps selected items of pipeline output data to a data sink entityrepresented by the selected data emitter component. Pipeline data thatcan be mapped in this manner can include raw or processed data from thedata source as well as analytic results generated by any models added tothe pipeline application using steps 1716-1732. At 1740, the pipelineapplication is configured to output the indicated pipeline output datato the data sink entity in accordance with the emitter configurationinput received at step 1738. If no selection of a data emitter componentis received (NO at step 1734), steps 1736-1740 are skipped. Themethodology then proceeds to the fourth part 1700 d illustrated in FIG.17 d.

At 1742, a determination is made as to whether an instruction to deploythe resulting pipeline application is received. If no such instructionis received (NO at step 1742), the methodology returns to step 1704, andsteps 1704-1742 are repeated to allow the user to add and configureadditional pipeline components or models as desired. Alternatively, ifan instruction to deploy the pipeline is received (YES at step 1742),the methodology proceeds to step 1744, where the pipeline applicationcreated using the preceding steps is compiled and deployed to one ormore data pipeline nodes for execution. Alternatively, the compiledpipeline application may execute on the same hardware platform on whichthe pipeline configuration system executes.

FIG. 18 illustrates an example methodology 1800 for executing anindustrial data pipeline application. Initially, at 1802, a pipelineapplication is received from a pipeline configuration system forexecution. The pipeline application can be generated using themethodology described above in connection with FIGS. 17a-17d . At 1804,data from a data source specified by the pipeline application isreceived. The data retrieved from the data source can be specified bydata source configuration information that is part of the application.At 1806, a schema of the incoming data received at step 1804 isidentified. In various scenarios, the data may be received in astructured or unstructured format that is not compatible with subsequentpipeline processing. At 1808, the incoming data is converted to a formatunderstandable by downstream pipeline processing element based onknowledge of the incoming data's schema and data preparation processingdefined by the pipeline application. This conversion yields transformeddata.

At 1810, selected items of the transformed data are input to an analyticmodel in accordance with a model mapping configuration defined by thepipeline application. At 1812, models scoring results are generatedbased on application of the analytic model to the selected items of thetransformed data input at step 1810. In some embodiments, the analyticmodel may execute on an application that is external to the hardwareplatform on which the pipeline application executes. In suchconfigurations, the items of transformed data can be sent to thisexternal application for processing by the model, and the model scoringresults can be returned to the data pipeline for further pipelineprocessing. Alternatively, the model may execute on the same hardwareplatform as the pipeline application, and the model scoring processingcan be performed locally.

At 1814, at least a subset of the model scoring results generated atstep 1812 and specified items of the transformed data generated at 1808are published to a data sink entity in accordance with an emitterconfiguration defined by the pipeline application. This data sink entitycan be, but is not limited to, a data repository (e.g., cloud-basedarchival storage), an industrial control system, a digital asset modelor digital twin of an industrial system used to facilitate control of anindustrial asset, a reporting or visualization application, an analyticapplication, or other such data destinations.

Embodiments, systems, and components described herein, as well ascontrol systems and automation environments in which various aspects setforth in the subject specification can be carried out, can includecomputer or network components such as servers, clients, programmablelogic controllers (PLCs), automation controllers, communicationsmodules, mobile computers, on-board computers for mobile vehicles,wireless components, control components and so forth which are capableof interacting across a network. Computers and servers include one ormore processors—electronic integrated circuits that perform logicoperations employing electric signals—configured to execute instructionsstored in media such as random access memory (RAM), read only memory(ROM), a hard drives, as well as removable memory devices, which caninclude memory sticks, memory cards, flash drives, external hard drives,and so on.

Similarly, the term PLC or automation controller as used herein caninclude functionality that can be shared across multiple components,systems, and/or networks. As an example, one or more PLCs or automationcontrollers can communicate and cooperate with various network devicesacross the network. This can include substantially any type of control,communications module, computer, Input/Output (I/O) device, sensor,actuator, and human machine interface (HMI) that communicate via thenetwork, which includes control, automation, and/or public networks. ThePLC or automation controller can also communicate to and control variousother devices such as standard or safety-rated I/O modules includinganalog, digital, programmed/intelligent I/O modules, other programmablecontrollers, communications modules, sensors, actuators, output devices,and the like.

The network can include public networks such as the internet, intranets,and automation networks such as control and information protocol (CIP)networks including DeviceNet, ControlNet, safety networks, andEthernet/IP. Other networks include Ethernet, DH/DH+, Remote I/O,Fieldbus, Modbus, Profibus, CAN, wireless networks, serial protocols,and so forth. In addition, the network devices can include variouspossibilities (hardware and/or software components). These includecomponents such as switches with virtual local area network (VLAN)capability, LANs, WANs, proxies, gateways, routers, firewalls, virtualprivate network (VPN) devices, servers, clients, computers,configuration tools, monitoring tools, and/or other devices.

In order to provide a context for the various aspects of the disclosedsubject matter, FIGS. 19 and 20 as well as the following discussion areintended to provide a brief, general description of a suitableenvironment in which the various aspects of the disclosed subject mattermay be implemented. While the embodiments have been described above inthe general context of computer-executable instructions that can run onone or more computers, those skilled in the art will recognize that theembodiments can be also implemented in combination with other programmodules and/or as a combination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the inventive methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, Internet of Things (IoT)devices, distributed computing systems, as well as personal computers,hand-held computing devices, microprocessor-based or programmableconsumer electronics, and the like, each of which can be operativelycoupled to one or more associated devices.

The illustrated embodiments herein can be also practiced in distributedcomputing environments where certain tasks are performed by remoteprocessing devices that are linked through a communications network. Ina distributed computing environment, program modules can be located inboth local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media, machine-readable storage media,and/or communications media, which two terms are used herein differentlyfrom one another as follows. Computer-readable storage media ormachine-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media or machine-readablestorage media can be implemented in connection with any method ortechnology for storage of information such as computer-readable ormachine-readable instructions, program modules, structured data orunstructured data.

Computer-readable storage media can include, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD), Blu-ray disc (BD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, solid state drives or other solid statestorage devices, or other tangible and/or non-transitory media which canbe used to store desired information. In this regard, the terms“tangible” or “non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and includes any information deliveryor transport media. The term “modulated data signal” or signals refersto a signal that has one or more of its characteristics set or changedin such a manner as to encode information in one or more signals. By wayof example, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

With reference again to FIG. 19 the example environment 1900 forimplementing various embodiments of the aspects described hereinincludes a computer 1902, the computer 1902 including a processing unit1904, a system memory 1906 and a system bus 1908. The system bus 1908couples system components including, but not limited to, the systemmemory 1906 to the processing unit 1904. The processing unit 1904 can beany of various commercially available processors. Dual microprocessorsand other multi-processor architectures can also be employed as theprocessing unit 1904.

The system bus 1908 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1906includes ROM 1910 and RAM 1912. A basic input/output system (BIOS) canbe stored in a non-volatile memory such as ROM, erasable programmableread only memory (EPROM), EEPROM, which BIOS contains the basic routinesthat help to transfer information between elements within the computer1902, such as during startup. The RAM 1912 can also include a high-speedRAM such as static RAM for caching data.

The computer 1902 further includes an internal hard disk drive (HDD)1914 (e.g., EIDE, SATA), one or more external storage devices 1916(e.g., a magnetic floppy disk drive (FDD) 1916, a memory stick or flashdrive reader, a memory card reader, etc.) and an optical disk drive 1920(e.g., which can read or write from a CD-ROM disc, a DVD, a BD, etc.).While the internal HDD 1914 is illustrated as located within thecomputer 1902, the internal HDD 1914 can also be configured for externaluse in a suitable chassis (not shown). Additionally, while not shown inenvironment 1900, a solid state drive (SSD) could be used in additionto, or in place of, an HDD 1914. The HDD 1914, external storagedevice(s) 1916 and optical disk drive 1920 can be connected to thesystem bus 1908 by an HDD interface 1924, an external storage interface1926 and an optical drive interface 1928, respectively. The interface1924 for external drive implementations can include at least one or bothof Universal Serial Bus (USB) and Institute of Electrical andElectronics Engineers (IEEE) 1394 interface technologies. Other externaldrive connection technologies are within contemplation of theembodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1902, the drives andstorage media accommodate the storage of any data in a suitable digitalformat. Although the description of computer-readable storage mediaabove refers to respective types of storage devices, it should beappreciated by those skilled in the art that other types of storagemedia which are readable by a computer, whether presently existing ordeveloped in the future, could also be used in the example operatingenvironment, and further, that any such storage media can containcomputer-executable instructions for performing the methods describedherein.

A number of program modules can be stored in the drives and RAM 1912,including an operating system 1930, one or more application programs1932, other program modules 1934 and program data 1936. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1912. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

Computer 1902 can optionally comprise emulation technologies. Forexample, a hypervisor (not shown) or other intermediary can emulate ahardware environment for operating system 1930, and the emulatedhardware can optionally be different from the hardware illustrated inFIG. 19. In such an embodiment, operating system 1930 can comprise onevirtual machine (VM) of multiple VMs hosted at computer 1902.Furthermore, operating system 1930 can provide runtime environments,such as the Java runtime environment or the .NET framework, forapplication programs 1932. Runtime environments are consistent executionenvironments that allow application programs 1932 to run on anyoperating system that includes the runtime environment. Similarly,operating system 1930 can support containers, and application programs1932 can be in the form of containers, which are lightweight,standalone, executable packages of software that include, e.g., code,runtime, system tools, system libraries and settings for an application.

Further, computer 1902 can be enable with a security module, such as atrusted processing module (TPM). For instance with a TPM, bootcomponents hash next in time boot components, and wait for a match ofresults to secured values, before loading a next boot component. Thisprocess can take place at any layer in the code execution stack ofcomputer 1902, e.g., applied at the application execution level or atthe operating system (OS) kernel level, thereby enabling security at anylevel of code execution.

A user can enter commands and information into the computer 1902 throughone or more wired/wireless input devices, e.g., a keyboard 1938, a touchscreen 1940, and a pointing device, such as a mouse 1942. Other inputdevices (not shown) can include a microphone, an infrared (IR) remotecontrol, a radio frequency (RF) remote control, or other remote control,a joystick, a virtual reality controller and/or virtual reality headset,a game pad, a stylus pen, an image input device, e.g., camera(s), agesture sensor input device, a vision movement sensor input device, anemotion or facial detection device, a biometric input device, e.g.,fingerprint or iris scanner, or the like. These and other input devicesare often connected to the processing unit 1904 through an input deviceinterface 1944 that can be coupled to the system bus 1908, but can beconnected by other interfaces, such as a parallel port, an IEEE 1394serial port, a game port, a USB port, an IR interface, a BLUETOOTH®interface, etc.

A monitor 1944 or other type of display device can be also connected tothe system bus 1908 via an interface, such as a video adapter 1946. Inaddition to the monitor 1944, a computer typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1902 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1948. The remotecomputer(s) 1948 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer1902, although, for purposes of brevity, only a memory/storage device1950 is illustrated. The logical connections depicted includewired/wireless connectivity to a local area network (LAN) 1952 and/orlarger networks, e.g., a wide area network (WAN) 1954. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 1902 can beconnected to the local network 1952 through a wired and/or wirelesscommunication network interface or adapter 1956. The adapter 1956 canfacilitate wired or wireless communication to the LAN 1952, which canalso include a wireless access point (AP) disposed thereon forcommunicating with the adapter 1956 in a wireless mode.

When used in a WAN networking environment, the computer 1902 can includea modem 1958 or can be connected to a communications server on the WAN1954 via other means for establishing communications over the WAN 1954,such as by way of the Internet. The modem 1958, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 1908 via the input device interface 1942. In a networkedenvironment, program modules depicted relative to the computer 1902 orportions thereof, can be stored in the remote memory/storage device1950. It will be appreciated that the network connections shown areexample and other means of establishing a communications link betweenthe computers can be used.

When used in either a LAN or WAN networking environment, the computer1902 can access cloud storage systems or other network-based storagesystems in addition to, or in place of, external storage devices 1916 asdescribed above. Generally, a connection between the computer 1902 and acloud storage system can be established over a LAN 1952 or WAN 1954e.g., by the adapter 1956 or modem 1958, respectively. Upon connectingthe computer 1902 to an associated cloud storage system, the externalstorage interface 1926 can, with the aid of the adapter 1956 and/ormodem 1958, manage storage provided by the cloud storage system as itwould other types of external storage. For instance, the externalstorage interface 1926 can be configured to provide access to cloudstorage sources as if those sources were physically connected to thecomputer 1902.

The computer 1902 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, store shelf, etc.), and telephone. This can include WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

FIG. 20 is a schematic block diagram of a sample computing environment2000 with which the disclosed subject matter can interact. The samplecomputing environment 2000 includes one or more client(s) 2002. Theclient(s) 2002 can be hardware and/or software (e.g., threads,processes, computing devices). The sample computing environment 200 alsoincludes one or more server(s) 2004. The server(s) 2004 can also behardware and/or software (e.g., threads, processes, computing devices).The servers 2004 can house threads to perform transformations byemploying one or more embodiments as described herein, for example. Onepossible communication between a client 2002 and servers 2004 can be inthe form of a data packet adapted to be transmitted between two or morecomputer processes. The sample computing environment 2000 includes acommunication framework 2006 that can be employed to facilitatecommunications between the client(s) 2002 and the server(s) 2004. Theclient(s) 2002 are operably connected to one or more client datastore(s) 2008 that can be employed to store information local to theclient(s) 2002. Similarly, the server(s) 2004 are operably connected toone or more server data store(s) 2010 that can be employed to storeinformation local to the servers 2004.

What has been described above includes examples of the subjectinnovation. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe disclosed subject matter, but one of ordinary skill in the art mayrecognize that many further combinations and permutations of the subjectinnovation are possible. Accordingly, the disclosed subject matter isintended to embrace all such alterations, modifications, and variationsthat fall within the spirit and scope of the appended claims.

In particular and in regard to the various functions performed by theabove described components, devices, circuits, systems and the like, theterms (including a reference to a “means”) used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., a functional equivalent), even though not structurallyequivalent to the disclosed structure, which performs the function inthe herein illustrated exemplary aspects of the disclosed subjectmatter. In this regard, it will also be recognized that the disclosedsubject matter includes a system as well as a computer-readable mediumhaving computer-executable instructions for performing the acts and/orevents of the various methods of the disclosed subject matter.

In addition, while a particular feature of the disclosed subject mattermay have been disclosed with respect to only one of severalimplementations, such feature may be combined with one or more otherfeatures of the other implementations as may be desired and advantageousfor any given or particular application. Furthermore, to the extent thatthe terms “includes,” and “including” and variants thereof are used ineither the detailed description or the claims, these terms are intendedto be inclusive in a manner similar to the term “comprising.”

In this application, the word “exemplary” is used to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Rather, use of the wordexemplary is intended to present concepts in a concrete fashion.

Various aspects or features described herein may be implemented as amethod, apparatus, or article of manufacture using standard programmingand/or engineering techniques. The term “article of manufacture” as usedherein is intended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. For example, computerreadable media can include but are not limited to magnetic storagedevices (e.g., hard disk, floppy disk, magnetic strips . . . ), opticaldisks [e.g., compact disk (CD), digital versatile disk (DVD) . . . ],smart cards, and flash memory devices (e.g., card, stick, key drive . .. ).

What is claimed is:
 1. A system, comprising: a memory that storesexecutable components; and a processor, operatively coupled to thememory, that executes the executable components, the executablecomponents comprising: a user interface component configured to renderan interface display and to receive, via interaction with the interfacedisplay, pipeline configuration input that defines aspects of a datapipeline, the pipeline configuration input comprising at least modelselection input that selects an analytic model to be included in thedata pipeline, and mapping input that maps selected items of input datafrom a data source to corresponding input fields of the analytic model;and a pipeline configuration component configured to generate pipelineapplication data based on the pipeline configuration input, wherein thepipeline application data configured to execute on a hardware platformto implement the data pipeline, and the data pipeline is configured toapply the analytic model to the selected items of the input data inaccordance with the pipeline configuration input.
 2. The system of claim1, wherein the interface display comprises a component selection sectionthat renders a library of pipeline components as selectable icons, and apipeline builder section that renders icons representing pipelinecomponents selected from the component selection section by the pipelineconfiguration input and the analytic model selected by the modelselection input as icons, and the pipeline configuration component isconfigured to generate the pipeline application data based on anarrangement of the icons rendered in the pipeline builder section. 3.The system of claim 1, wherein the user interface component isconfigured to: render, on the interface display, a connection name fieldthat prompts for information specifying a connection path to anapplication on which one or more analytic models are stored, and render,on the interface display, a list of the one or more analytic models,including the analytic model, based on the information specifying theconnection path, and wherein the model selection input selects theanalytic model from the list.
 4. The system of claim 1, furthercomprising a model mapping component configured to, in response toselection of the analytic model by the model selection input, identifyinput fields defined by the analytic model, wherein the user interfacecomponent is configured to render a field mapping interface display thatdisplays the input fields identified by the model mapping component, anddata input selection fields respectively corresponding to the inputfields, the mapping input selects a pipeline data item via interactionwith one of the data input selection fields, and selection of thepipeline data item by the mapping input maps the pipeline data item toone of the input fields of the analytic model corresponding to the oneof the data input selection fields.
 5. The system of claim 4, wherein adata input selection field, of the data input selection fields, listspipeline data items available to be selectively mapped to one of theinput fields of the analytic model, and the user interface componentdetermines the pipeline data items to be listed in the data inputselection field based on the data source for the data pipeline specifiedby the pipeline configuration input.
 6. The system of claim 5, whereinthe pipeline configuration input further comprises at least datapreparation selection input that selects a data preparation component,from a library of pipeline components, for inclusion in the pipelineapplication data, the inclusion of the data preparation component in thepipeline application data configures the data pipeline to convert inputdata from the data source to transformed data having a format that isunderstandable by processing components of the data pipeline, and theuser interface component determines the pipeline data items to be listedin the data input selection field based on the transformed data.
 7. Thesystem of claim 1, further comprising a model scoring componentconfigured to generate model scoring result data based on application ofthe analytic model to the selected items of the input data in accordancewith the mapping input.
 8. The system of claim 7, wherein the userinterface component is configured to render a model configurationinterface display that displays configurable model parameters defined bythe analytic model, the pipeline configuration input further comprisesmodel configuration input that sets values of the configurable modelparameters, and the model scoring component is configured to generatethe scoring result data in accordance with the values of theconfigurable model parameters.
 9. The system of claim 7, wherein thepipeline configuration input further comprises at least emitterselection input that selects an emitter component, from a library ofpipeline components, for inclusion in the pipeline application data, andinclusion of the emitter component in the pipeline application dataconfigures the data pipeline to publish the model scoring result data toa data sink entity represented by the emitter component.
 10. The systemof claim 1, further comprising a pipeline deployment componentconfigured to deploy the pipeline application data to the hardwareplatform.
 11. A method, comprising: rendering, by a system comprising aprocessor, an interface display on a client device; receiving, by thesystem via interaction with the interface display, pipelineconfiguration input that defines aspects of a data pipeline, wherein thereceiving comprises: receiving at least model selection input thatselects an analytic or machine learning model to be included in the datapipeline, and receiving mapping input that maps selected items of inputdata from a data source to respective input fields of the analytic ormachine learning model; and generating, by the system, a data pipelineapplication in accordance with the pipeline configuration input, whereinthe generating comprises, based on the model selection and the mappinginput, configuring the data pipeline application to apply the analyticor machine learning model to the selected items of the input data, andthe data pipeline application is configured to execute on a hardwaredevice to implement the data pipeline.
 12. The method of claim 11,wherein the rendering of the interface display comprises formatting theinterface display to include a component selection section that rendersa library of pipeline objects as selectable icons representing thepipeline components, and a pipeline builder section that renders iconsrepresenting pipeline components selected from the component selectionsection by the pipeline configuration input and the analytic or machinelearning model selected by the model selection input, and the generatingof the data pipeline application comprises generating the data pipelineapplication based on an arrangement of the icons rendered in thepipeline builder section.
 13. The method of claim 11, wherein therendering of the interface display comprises: rendering, on theinterface display, a connection name field that prompts for informationspecifying a connection path to an application on which one or moreanalytic or machine learning models are stored, and rendering, on theinterface display, a list of the one or more analytic or machinelearning models based on the information specifying the connection path,wherein the model selection input selects the analytic or machinelearning model via interaction with the list.
 14. The method of claim11, further comprising: in response to receiving the model selectioninput: identifying, by the system, input fields of the analytic ormachine learning model, and rendering, by the system, field mappinginterface that displays the input fields identified by the model mappingcomponent and data input selection fields respectively corresponding tothe input fields; and in response to receiving the mapping input viainteraction with one of the data input selection fields: identifying, bythe system, a pipeline data item specified by the mapping input, andmapping, by the system, the pipeline data item to one of the inputfields of the analytic or machine learning model corresponding to theone of the data input selection fields.
 15. The method of claim 14,wherein the rendering of the field mapping interface comprises:identifying pipeline data items that are available to the data pipelinebased on an identity of the data source, and rendering the pipeline dataitems available for selection via the one of the data input selectionfields.
 16. The method of claim 11, further comprising: applying, by thesystem, the analytic or machine learning model to the selected items ofthe input data in accordance with the mapping input; and generating, bythe system, model scoring result data based the applying of the analyticor machine learning model to the selected items of the input data. 17.The method of claim 16, wherein the rendering of the interface displaycomprises rendering configurable model parameters defined by theanalytic or machine learning model, the receiving of the pipelineconfiguration input comprises receiving model configuration input thatsets values of the configurable model parameters, and the applying theanalytic or machine learning model comprises applying the analytic ormachine learning model in accordance with the values of the configurablemodel parameters.
 18. The method of claim 16, wherein the receiving ofthe pipeline configuration input further comprises receiving emitterselection input that selects an emitter component, from a library ofpipeline components, for inclusion in the pipeline configurationapplication, and the generating comprises, based on the emitterselection input, configuring the data pipeline application to publishthe model scoring result data to a data sink entity represented by theemitter component.
 19. A non-transitory computer-readable medium havingstored thereon instructions that, in response to execution, cause asystem comprising a processor to perform operations, the operationscomprising: rendering an interface display on a client device;receiving, via interaction with the interface display, pipelineconfiguration input that defines aspects of a data pipeline, wherein thereceiving comprises: receiving at least model selection input thatselects an analytic or machine learning model to be included in the datapipeline, and receiving mapping input that maps selected items of inputdata from a data source to respective input fields of the analytic ormachine learning model; and generating a data pipeline application inaccordance with the pipeline configuration input, wherein the generatingcomprises, based on the model selection and the mapping input,configuring the data pipeline application to apply the analytic ormachine learning model to the selected items of the input data, and thedata pipeline application is configured to execute on a hardwareplatform to operate the data pipeline.
 20. The non-transitorycomputer-readable medium of claim 19, wherein the rendering of theinterface display comprises: rendering, on the interface display, aconnection name field that prompts for information specifying aconnection path to an application on which one or more analytic ormachine learning models are stored, and rendering, on the interfacedisplay, a list of the one or more analytic or machine learning modelsbased on the information specifying the connection path, wherein themodel selection input selects the analytic or machine learning model viainteraction with the list.